#DataScaling

Live, measured metrics for the hashtag #DataScaling from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.

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0
Uses / 7 days
Mastodon
0
Accounts / 7 days
Mastodon
2
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 2
0
Avg reactions / post
Mastodon · last 2

Day-by-day usage

measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-27 23:06 UTC
0
07-21
0
07-22
0
07-23
0
07-24
0
07-25
0
07-26
0
07-27

0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.

Related hashtags

measured · fosstodon.org (Mastodon public search API) · fetched 2026-07-27 23:06 UTC

No related tags with measured usage found for #datascaling.

Live pulse

measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-27 23:06 UTC

Everything below is measured over the latest 2 public posts (spanning ~364 hours).

Posting hours (UTC)

00:0012:0023:00

Languages: English (2)

Avg boosts / post: 0

Top of the latest posts

  • Litian Liang (@litian_liang) 딥러닝에서 '더 많은 데이터는 해가 되지 않는다'는 거의 반박하기 어려운 명제지만, 로봇 모델에서는 어느 규모에서 데이터 추가가 성능 대비 가장 높은 ROI를 주는지가 아직 핵심 질문이라고 지적한다. Ilyas의 강연을 추천하며 로봇 학습에서 데이터 스케일링의 효율이 중요하다고 강조한다. https://x.com/litian_liang/status/206877253320228

    ainews@[email protected]♥ 0↻ 02026-06-22 04:47 UTCView post →

  • Sudo su (@sudoingX) 훈련 예시 수를 늘리는 데이터 스케일링이 단순히 더 큰 네트워크를 만드는 것보다 일반화 성능에 유리하다는 점을 차트로 설명합니다. 학습 데이터에는 96%까지 올라가지만 보지 못한 데이터에서는 성능이 갈리는 모습을 보여, 소규모 데이터에서의 과적합과 데이터 확장의 중요성을 강조합니다. https://x.com/sudoingX/status/2063337880613966012 #datascalin

    ainews@[email protected]♥ 0↻ 02026-06-07 00:49 UTCView post →

Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/datascaling